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dc.contributor.authorYger, Florian
dc.contributor.authorBerar, Maxime
dc.contributor.authorLotte, Fabien
dc.date.accessioned2017-02-06T17:07:41Z
dc.date.available2017-02-06T17:07:41Z
dc.date.issued2017
dc.identifier.issn1534-4320
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16233
dc.language.isoenen
dc.subjectBrain-Computer Interface (BCI)en
dc.subjectRiemannian geometryen
dc.subjectcovariance matricesen
dc.subjectsubspacesen
dc.subjectsource extractionen
dc.subjectElectroencephalography (EEG)en
dc.subjectclassificationen
dc.subject.ddc006.3; 516en
dc.titleRiemannian approaches in Brain-Computer Interfaces: a reviewen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenAlthough promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.en
dc.relation.isversionofjnlnameIEEE Transactions on Neural System and Rehabilitation Engineering
dc.relation.isversionofjnlissue99en
dc.relation.isversionofjnldate2016-11
dc.relation.isversionofdoi10.1109/TNSRE.2016.2627016en
dc.contributor.countryeditoruniversityotherFRANCE
dc.subject.ddclabelIntelligence artificielle; Géométrieen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-02-06T16:43:17Z
hal.person.labIds989
hal.person.labIds23832
hal.person.labIds3102


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